In this section, we ablate the effectiveness of our method on alleviating human labor, by training on small-size datasets but with a much longer training schedule. Specifically, we use the same down-sampled COCO 2017 dataset in Section  of our main text. But we increase the number of training epochs to match the computational cost of full COCO 2017 dataset training. The experiments are based on CondDETR [1]} variants. All models are trained with a batch size of 32 and the learning rate is decayed after training for 0.8 times the total training epochs.
